Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

Diana    M.    Herrera-Ibatá
doi:10.2174/1568026621666210121153413
Abstract

Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing significant amounts of chemical data obtaining outcomes to find potential drug candidates.
Keywords: Perturbation theory, Machine learning, Drug discovery, Protein targets, New materials, ChEMBL.
« Previous Next »
Graphical Abstract

[1] 
Tian, K.; Shao, M.; Wang, Y.; Guan, J.; Zhou, S. Boosting compound-protein interaction prediction by deep learning. Methods,  2016, 110, 64-72.
[http://dx.doi.org/10.1016/j.ymeth.2016.06.024] [PMID: 27378654] 
[2] 
Rifaioglu, A.S.; Nalbat, E.; Atalay, V.; Martin, M.J.; Cetin-Atalay, R.; Doğan, T. DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem. Sci. (Camb.),  2020, 11(9), 2531-2557.
[http://dx.doi.org/10.1039/C9SC03414E] [PMID: 33209251] 
[3] 
Duch, W.; Swaminathan, K.; Meller, J. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des.,  2007, 13(14), 1497-1508.
[http://dx.doi.org/10.2174/138161207780765954] [PMID: 17504169] 
[4] 
Lusci, A.; Pollastri, G.; Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model.,  2013, 53(7), 1563-1575.
[http://dx.doi.org/10.1021/ci400187y] [PMID: 23795551] 
[5] 
Maltarollo, V.G.; Gertrudes, J.C.; Oliveira, P.R.; Honorio, K.M. Applying machine learning techniques for ADME-Tox prediction: a review. Expert Opin. Drug Metab. Toxicol.,  2015, 11(2), 259-271.
[http://dx.doi.org/10.1517/17425255.2015.980814] [PMID: 25440524] 
[6] 
Lipinski, C.F.; Maltarollo, V.G.; Oliveira, P.R.; da Silva, A.B.F.; Honorio, K.M. Advances and perspectives in applying deep learning for drug design and discovery. Front. Robot. AI,  2019, 6, 108.
[http://dx.doi.org/10.3389/frobt.2019.00108] [PMID: 33501123] 
[7] 
Lo, Y-C.; Rensi, S.E.; Torng, W.; Altman, R.B. Machine learning in chemoinformatics and drug discovery. Drug Discov. Today,  2018, 23(8), 1538-1546.
[http://dx.doi.org/10.1016/j.drudis.2018.05.010] [PMID: 29750902] 
[8] 
Dudek, A.Z.; Arodz, T.; Gálvez, J. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Throughput Screen.,  2006, 9(3), 213-228.
[http://dx.doi.org/10.2174/138620706776055539] [PMID: 16533155] 
[9] 
Wiener, H. Structural determination of paraffin boiling points. J. Am. Chem. Soc.,  1947, 69(1), 17-20.
[http://dx.doi.org/10.1021/ja01193a005] [PMID: 20291038] 
[10] 
Balaban, A.T. Highly discriminating distance-based topological index. Chem. Phys. Lett.,  1982, 89, 399-404.
[http://dx.doi.org/10.1016/0009-2614(82)80009-2] 
[11] 
Labute, P. A widely applicable set of descriptors. J. Mol. Graph. Model.,  2000, 18(4-5), 464-477.
[http://dx.doi.org/10.1016/S1093-3263(00)00068-1] [PMID: 11143563] 
[12] 
D’Souza, S.K.V.P.S.B. Machine learning in drug-target interaction prediction: current state and future directions. Drug Discov. Today,  2020, 25(4), 748-756.
[http://dx.doi.org/10.1016/j.drudis.2020.03.003] [PMID: 32171918] 
[13] 
Alonso, N.; Caamaño, O.; Romero-Duran, F.J.; Luan, F.; D S Cordeiro, M.N.; Yañez, M.; González-Díaz, H.; García-Mera, X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem. Neurosci.,  2013, 4(10), 1393-1403.
[http://dx.doi.org/10.1021/cn400111n] [PMID: 23855599] 
[14] 
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg. Med. Chem.,  2012, 20(15), 4848-4855.
[http://dx.doi.org/10.1016/j.bmc.2012.05.071] [PMID: 22750007] 
[15] 
Prado-Prado, F.J.; García, I.; García-Mera, X.; González-Díaz, H. Entropy multi-target qsar model for prediction of antiviral drug complex networks. Chemom. Intell. Lab. Syst.,  2011, 107, 227-233.
[http://dx.doi.org/10.1016/j.chemolab.2011.02.003] 
[16] 
Chen, H.; Kogej, T.; Engkvist, O. Cheminformatics in drug discovery, an industrial perspective. Mol. Inform.,  2018, 37(9-10), e1800041.
[http://dx.doi.org/10.1002/minf.201800041] [PMID: 29774657] 
[17] 
Gertrudes, J.C.; Maltarollo, V.G.; Silva, R.A.; Oliveira, P.R.; Honório, K.M.; da Silva, A.B.F. Machine learning techniques and drug design. Curr. Med. Chem.,  2012, 19(25), 4289-4297.
[http://dx.doi.org/10.2174/092986712802884259] [PMID: 22830342] 
[18] 
Lima, A.N.; Philot, E.A.; Trossini, G.H.G.; Scott, L.P.B.; Maltarollo, V.G.; Honorio, K.M. Use of machine learning approaches for novel drug discovery. Expert Opin. Drug Discov.,  2016, 11(3), 225-239.
[http://dx.doi.org/10.1517/17460441.2016.1146250] [PMID: 26814169] 
[19] 
Verikas, A.; Gelzinis, A.; Bacauskiene, M. Mining data with random forests: a survey and results of new tests. Pattern Recognit.,  2011, 44, 330-349.
[http://dx.doi.org/10.1016/j.patcog.2010.08.011] 
[20] 
Breiman, L. Random forests. Mach. Learn.,  2001, 45, 5-32.
[http://dx.doi.org/10.1023/A:1010933404324] 
[21] 
Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci.,  2003, 43(6), 1882-1889.
[http://dx.doi.org/10.1021/ci0341161] [PMID: 14632437] 
[22] 
Zernov, V.V.; Balakin, K.V.; Ivaschenko, A.A.; Savchuk, N.P.; Pletnev, I.V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci.,  2003, 43(6), 2048-2056.
[http://dx.doi.org/10.1021/ci0340916] [PMID: 14632457] 
[23] 
Warmuth, M.K.; Liao, J.; Rätsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci.,  2003, 43(2), 667-673.
[http://dx.doi.org/10.1021/ci025620t] [PMID: 12653536] 
[24] 
Jorissen, R.N.; Gilson, M.K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model.,  2005, 45(3), 549-561.
[http://dx.doi.org/10.1021/ci049641u] [PMID: 15921445] 
[25] 
Carpenter, K.A.; Huang, X. Machine learning-based virtual screening and its applications to alzheimer’s drug discovery: a review. Curr. Pharm. Des.,  2018, 24(28), 3347-3358.
[http://dx.doi.org/10.2174/1381612824666180607124038] [PMID: 29879881] 
[26] 
Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory,  1967, 13, 21-27.
[http://dx.doi.org/10.1109/TIT.1967.1053964] 
[27] 
Haykin, S. Neural networks: a comprehensive foundation. In: Prentice
 Hall: New Jersey; , 1994. 
[28] 
Batool, M.; Ahmad, B.; Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci.,  2019, 20(11), 2783.
[http://dx.doi.org/10.3390/ijms20112783] [PMID: 31174387] 
[29] 
van Gerven, M.; Bohte, S. Editorial: artificial neural networks as models of neural information processing. Front. Comput. Neurosci.,  2017, 11, 114.
[http://dx.doi.org/10.3389/fncom.2017.00114] [PMID: 29311884] 
[30] 
Goh, G.B.; Hodas, N.O.; Vishnu, A. Deep learning for computational chemistry. J. Comput. Chem.,  2017, 38(16), 1291-1307.
[http://dx.doi.org/10.1002/jcc.24764] [PMID: 28272810] 
[31] 
Fernández, C.; Soria, E.; Martín, J.D.; Serrano, A.J. Neural networks for animal science applications: two case studies. Expert Syst. Appl.,  2006, 31, 444-450.
[http://dx.doi.org/10.1016/j.eswa.2005.09.086] 
[32] 
Seker, H.; Odetayo, M.O.; Petrovic, D.; Naguib, R.N.G.; Bartoli, C.; Alasio, L.; Lakshmi, M.S.; Sherbet, G.V. Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches. Anticancer Res.,  2002, 22(1A), 433-438.
[PMID: 12017328] 
[33] 
Li, H.; Yap, C.W.; Xue, Y.; Li, Z.R.; Ung, C.Y.; Han, L.Y.; Chen, Y.Z. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents. Drug Dev. Res.,  2005, 66, 245-259.
[http://dx.doi.org/10.1002/ddr.20044] 
[34] 
Mager, D.E.; Shirey, J.D.; Cox, D.; Fitzgerald, D.J.; Abernethy, D.R. Mapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network. J. Pharm. Sci.,  2005, 94(11), 2475-2486.
[http://dx.doi.org/10.1002/jps.20384] [PMID: 16200613] 
[35] 
Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J.; Cibrián-Uhalte, E.; Davies, M.; Dedman, N.; Karlsson, A.; Magariños, M.P.; Overington, J.P.; Papadatos, G.; Smit, I.; Leach, A.R. The ChEMBL database in 2017. Nucleic Acids Res.,  2017, 45(D1), D945-D954.
[http://dx.doi.org/10.1093/nar/gkw1074] [PMID: 27899562] 
[36] 
Casañola-Martin, G.M.; Le-Thi-Thu, H.; Pérez-Giménez, F.; Marrero-Ponce, Y.; Merino-Sanjuán, M.; Abad, C.; González-Díaz, H. Multi-output model with box-jenkins operators of quadratic indices for prediction of malaria and cancer inhibitors targeting ubiquitin- proteasome pathway (upp) proteins. Curr. Protein Pept. Sci.,  2016, 17(3), 220-227.
[http://dx.doi.org/10.2174/1389203717999160226173500] [PMID: 26427384] 
[37] 
Blázquez-Barbadillo, C.; Aranzamendi, E.; Coya, E.; Lete, E.; Sotomayor, N.; González-Díaz, H. Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed heck–heck cascade reactions. RSC Advances,  2016, 6, 38602-38610.
[http://dx.doi.org/10.1039/C6RA08751E] 
[38] 
Romero-Durán, F.J.; Alonso, N.; Yañez, M.; Caamaño, O.; García-Mera, X.; González-Díaz, H. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology,  2016, 103, 270-278.
[http://dx.doi.org/10.1016/j.neuropharm.2015.12.019] [PMID: 26721628] 
[39] 
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ. Sci. Technol.,  2014, 48(24), 14686-14694.
[http://dx.doi.org/10.1021/es503861x] [PMID: 25384130] 
[40] 
Luan, F.; Kleandrova, V.V.; González-Díaz, H.; Ruso, J.M.; Melo, A.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale,  2014, 6(18), 10623-10630.
[http://dx.doi.org/10.1039/C4NR01285B] [PMID: 25083742] 
[41] 
González-Díaz, H.; Arrasate, S.; Gómez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J.M. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr. Top. Med. Chem.,  2013, 13(14), 1713-1741.
[http://dx.doi.org/10.2174/1568026611313140011] [PMID: 23889050] 
[42] 
Martínez-Arzate, S.G.; Tenorio-Borroto, E.; Barbabosa Pliego, A.; Díaz-Albiter, H.M.; Vázquez-Chagoyán, J.C.; González-Díaz, H. PTML model for proteome mining of b-cell epitopes and theoretical-experimental study of bm86 protein sequences from colima, Mexico. J. Proteome Res.,  2017, 16(11), 4093-4103.
[http://dx.doi.org/10.1021/acs.jproteome.7b00477] [PMID: 28922600] 
[43] 
Vásquez-Domínguez, E.; Armijos-Jaramillo, V.D.; Tejera, E.; González-Díaz, H. Multioutput perturbation-theory machine learning (PTML) model of chembl data for antiretroviral compounds. Mol. Pharm.,  2019, 16(10), 4200-4212.
[http://dx.doi.org/10.1021/acs.molpharmaceut.9b00538] [PMID: 31426639] 
[44] 
Nocedo-Mena, D.; Cornelio, C.; Camacho-Corona, M.D.R.; Garza-González, E.; Waksman de Torres, N.; Arrasate, S.; Sotomayor, N.; Lete, E.; González-Díaz, H. Modeling antibacterial activity with machine learning and fusion of chemical structure information with microorganism metabolic networks. J. Chem. Inf. Model.,  2019, 59(3), 1109-1120.
[http://dx.doi.org/10.1021/acs.jcim.9b00034] [PMID: 30802402] 
[45] 
Santana, R.; Zuluaga, R.; Gañán, P.; Arrasate, S.; Onieva, E.; González-Díaz, H. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models. Nanoscale,  2019, 11(45), 21811-21823.
[http://dx.doi.org/10.1039/C9NR05070A] [PMID: 31691701] 
[46] 
Gonzalez-Diaz, H. PTML: Perturbation-Theory Machine Learning Notes.  Proceedings of the Proceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition; MDPI: Switzerland, 2018, p. 5463.
[http://dx.doi.org/10.3390/mol2net-04-05463] 
[47] 
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. 
                        Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the
                        
                             in silico
                        
                         discovery of anti-breast cancer agents.
                     Eur. J. Pharm. Sci.,  2012, 47(1), 273-279.
[http://dx.doi.org/10.1016/j.ejps.2012.04.012] [PMID: 22538055] 
[48] 
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer. Agents Med. Chem.,  2013, 13(5), 791-800.
[http://dx.doi.org/10.2174/1871520611313050013] [PMID: 23272967] 
[49] 
Speck-Planche, A.; Cordeiro, M.N.D.S. Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol. Divers.,  2017, 21(3), 511-523.
[http://dx.doi.org/10.1007/s11030-017-9731-1] [PMID: 28194627] 
[50] 
Bediaga, H.; Arrasate, S.; González-Díaz, H. PTML Combinatorial model of chembl compounds assays for multiple types of cancer. ACS Comb. Sci.,  2018, 20(11), 621-632.
[http://dx.doi.org/10.1021/acscombsci.8b00090] [PMID: 30240186] 
[51] 
Sayers, E.W.; Cavanaugh, M.; Clark, K.; Ostell, J.; Pruitt, K.D.; Karsch-Mizrachi, I. GenBank. Nucleic Acids Res.,  2019, 47(D1), D94-D99.
[http://dx.doi.org/10.1093/nar/gky989] [PMID: 30365038] 
[52] 
González-Díaz, H.; Herrera-Ibatá, D.M.; Duardo-Sánchez, A.; Munteanu, C.R.; Orbegozo-Medina, R.A.; Pazos, A. ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. J. Chem. Inf. Model.,  2014, 54(3), 744-755.
[http://dx.doi.org/10.1021/ci400716y] [PMID: 24521170] 
[53] 
Herrera-Ibatá, D.M.; Pazos, A.; Orbegozo-Medina, R.A.; González-Díaz, H. Mapping networks of anti-hiv drug cocktails vs. aids epidemiology in the US counties. Chemom. Intell. Lab. Syst.,  2014, 138, 161-170.
[http://dx.doi.org/10.1016/j.chemolab.2014.08.006] 
[54] 
Herrera-Ibatá, D.M.; Pazos, A.; Orbegozo-Medina, R.A.; Romero-Durán, F.J.; González-Díaz, H. Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Biosystems,  2015, 132-133, 20-34.
[http://dx.doi.org/10.1016/j.biosystems.2015.04.007] [PMID: 25916548] 
[55] 
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations. J. Comput. Chem.,  2010, 31(2), 381-392.
[PMID: 19479738] 
[56] 
González-Díaz, H.; Arrasate, S.; Sotomayor, N.; Lete, E.; Munteanu, C.R.; Pazos, A.; Besada-Porto, L.; Ruso, J.M. MIANN models in medicinal, physical and organic chemistry. Curr. Top. Med. Chem.,  2013, 13(5), 619-641.
[http://dx.doi.org/10.2174/1568026611313050006] [PMID: 23548024] 
[57] 
Ran, T.; Liu, Y.; Li, H.; Tang, S.; He, Z.; Munteanu, C.R.; González-Díaz, H.; Tan, Z.; Zhou, C. Gastrointestinal spatiotemporal mrna expression of ghrelin vs growth hormone receptor and new growth yield machine learning model based on perturbation theory. Sci. Rep.,  2016, 6, 30174.
[http://dx.doi.org/10.1038/srep30174] [PMID: 27460882] 
[58] 
Messina, P.V.; Besada-Porto, J.M.; González-Díaz, H.; Ruso, J.M. Self-assembled binary nanoscale systems: multioutput model with lfer-covariance perturbation theory and an experimental-computational study of nagdc-ddab micelles. Langmuir,  2015, 31(44), 12009-12018.
[http://dx.doi.org/10.1021/acs.langmuir.5b03074] [PMID: 26484726] 
[59] 
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Melo, A.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ. Int.,  2014, 73, 288-294.
[http://dx.doi.org/10.1016/j.envint.2014.08.009] [PMID: 25173945] 
[60] 
Ferreira da Costa, J.; Silva, D.; Caamaño, O.; Brea, J.M.; Loza, M.I.; Munteanu, C.R.; Pazos, A.; García-Mera, X.; González-Díaz, H. Perturbation theory/machine learning model of chembl data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics. ACS Chem. Neurosci.,  2018, 9(11), 2572-2587.
[http://dx.doi.org/10.1021/acschemneuro.8b00083] [PMID: 29791132] 
[61] 
Ivanenkov, Y.A.; Zhavoronkov, A.; Yamidanov, R.S.; Osterman, I.A.; Sergiev, P.V.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Veselov, M.S.; Ayginin, A.A.; Kartsev, V.G.; Skvortsov, D.A.; Chemeris, A.V.; Baimiev, A.K.; Sofronova, A.A.; Malyshev, A.S.; Filkov, G.I.; Bezrukov, D.S.; Zagribelnyy, B.A.; Putin, E.O.; Puchinina, M.M.; Dontsova, O.A. Identification of novel antibacterials using machine learning techniques. Front. Pharmacol.,  2019, 10, 913.
[http://dx.doi.org/10.3389/fphar.2019.00913] [PMID: 31507413] 
[62] 
Li, M.M.; Sengupta, S.; Hanigan, M.D. Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen. J. Dairy Sci.,  2019, 102(10), 8850-8861.
[http://dx.doi.org/10.3168/jds.2018-15964] [PMID: 31378500] 
[63] 
Wei, Y.; Li, W.; Du, T.; Hong, Z.; Lin, J. Targeting hiv/hcv coinfection using a machine learning-based multiple quantitative structure-activity relationships (multiple qsar) method. Int. J. Mol. Sci.,  2019, 20(14), 20.
[http://dx.doi.org/10.3390/ijms20143572] [PMID: 31336592] 
[64] 
Li, Y.H.; Yu, C.Y.; Li, X.X.; Zhang, P.; Tang, J.; Yang, Q.; Fu, T.; Zhang, X.; Cui, X.; Tu, G.; Zhang, Y.; Li, S.; Yang, F.; Sun, Q.; Qin, C.; Zeng, X.; Chen, Z.; Chen, Y.Z.; Zhu, F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res.,  2018, 46(D1), D1121-D1127.
[http://dx.doi.org/10.1093/nar/gkx1076] [PMID: 29140520] 
[65] 
Redkar, S.; Mondal, S.; Joseph, A.; Hareesha, K.S. A machine learning approach for drug-target interaction prediction using wrapper feature selection and class balancing. Mol. Inform.,  2020, 39(5), e1900062.
[http://dx.doi.org/10.1002/minf.201900062] [PMID: 32003548] 
[66] 
Wang, Z.; Yang, H.; Wu, Z.; Wang, T.; Li, W.; Tang, Y.; Liu, G. 
                            In silico
                        
                         prediction of blood-brain barrier permeability of compounds by machine learning and resampling methods.
                     Chem Med Chem,  2018, 13(20), 2189-2201.
[http://dx.doi.org/10.1002/cmdc.201800533] [PMID: 30110511] 
[67] 
Zhang, M.; Xia, Z.; Yan, A. Computer modeling in predicting the bioactivity of human 5-lipoxygenase inhibitors. Mol. Divers.,  2017, 21(1), 235-246.
[http://dx.doi.org/10.1007/s11030-016-9709-4] [PMID: 27904990] 
[68] 
Xiao, Y-D.; Harris, R.; Bayram, E.; Ii, P.S.; Schmitt, J.D. Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation. J. Chem. Inf. Model.,  2006, 46(1), 137-144.
[http://dx.doi.org/10.1021/ci0500841] [PMID: 16426050] 
[69] 
Luque Ruiz, I.; Gómez-Nieto, M.Á. QSAR classification and regression models for β-secretase inhibitors using relative distance matrices. SAR QSAR Environ. Res.,  2018, 29(5), 355-383.
[http://dx.doi.org/10.1080/1062936X.2018.1442879] [PMID: 29514505] 
[70] 
Sahu, S.; Ghosh, S.K.; Kalita, J.M.; Ginjupalli, M.C.; K, K.R. Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models. Eur. J. Pharm. Sci.,  2020, 144, 105208.
[http://dx.doi.org/10.1016/j.ejps.2019.105208] [PMID: 31883446] 
[71] 
Ancuceanu, R.; Hovanet, M.V.; Anghel, A.I.; Furtunescu, F.; Neagu, M.; Constantin, C.; Dinu, M. Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the dilirank dataset. Int. J. Mol. Sci.,  2020, 21(6), 2114.
[http://dx.doi.org/10.3390/ijms21062114] [PMID: 32204453] 
[72] 
 R. R Development Core Team. A language and environment for statistical computing. R Foundation of Statistical Computing: Vienna, 2013.
[73] 
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model.,  2012, 52(7), 1757-1768.
[http://dx.doi.org/10.1021/ci3001277] [PMID: 22587354] 
[74] 
González-Díaz, H.; Riera-Fernández, P. New Markov-autocorrelation indices for re-evaluation of links in chemical and biological complex networks used in metabolomics, parasitology, neurosciences, and epidemiology. J. Chem. Inf. Model.,  2012, 52(12), 3331-3340.
[http://dx.doi.org/10.1021/ci300321f] [PMID: 23121444] 
[75] 
Simón-Vidal, L.; García-Calvo, O.; Oteo, U.; Arrasate, S.; Lete, E.; Sotomayor, N.; González-Díaz, H. Perturbation-theory and machine learning (PTML) model for high-throughput screening of parham reactions: experimental and theoretical studies. J. Chem. Inf. Model.,  2018, 58(7), 1384-1396.
[http://dx.doi.org/10.1021/acs.jcim.8b00286] [PMID: 29898360] 
[76] 
Aranzamendi, E.; Arrasate, S.; Sotomayor, N.; González-Díaz, H.; Lete, E. Chiral brønsted acid-catalyzed enantioselective α-amidoalkylation reactions: a joint experimental and predictive study. ChemistryOpen,  2016, 5(6), 540-549.
[http://dx.doi.org/10.1002/open.201600120] [PMID: 28032023] 
[77] 
Santana, R.; Zuluaga, R.; Gañán, P.; Arrasate, S.; Onieva Caracuel, E.; González-Díaz, H. PTML model of chembl compounds assays for vitamin derivatives. ACS Comb. Sci.,  2020, 22(3), 129-141.
[http://dx.doi.org/10.1021/acscombsci.9b00166] [PMID: 32011854] 
[78] 
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc.,  2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332] 
[79] 
Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis c leads. ACS Comb. Sci.,  2017, 19(8), 501-512.
[http://dx.doi.org/10.1021/acscombsci.7b00039] [PMID: 28437091] 
[80] 
Tenorio-Borroto, E.; Castañedo, N.; García-Mera, X.; Rivadeneira, K.; Vázquez Chagoyán, J.C.; Barbabosa Pliego, A.; Munteanu, C.R.; González-Díaz, H. Perturbation theory machine learning modeling of immunotoxicity for drugs targeting inflammatory cytokines and study of the antimicrobial g1 using cytometric bead arrays. Chem. Res. Toxicol.,  2019, 32(9), 1811-1823.
[http://dx.doi.org/10.1021/acs.chemrestox.9b00154] [PMID: 31327231] 
[81] 
Ambure, P.; Halder, A.K.; González Díaz, H.; Cordeiro, M.N.D.S. QSAR-Co: an open source software for developing robust multitasking or multitarget classification-based qsar models. J. Chem. Inf. Model.,  2019, 59(6), 2538-2544.
[http://dx.doi.org/10.1021/acs.jcim.9b00295] [PMID: 31083984] 
[82] 
Liu, Y.; Munteanu, C.R.; Fernandez-Lozano, C.; Pazos, A.; Ran, T.; Tan, Z.; Yu, Y.; Zhou, C.; Tang, S.; González-Díaz, H. Experimental study and ann dual-time scale perturbation model of electrokinetic properties of microbiota. Front. Microbiol.,  2017, 8, 1216.
[http://dx.doi.org/10.3389/fmicb.2017.01216] [PMID: 28713345] 
[83] 
Concu, R.; D S Cordeiro, M.N.; Munteanu, C.R.; González-Díaz, H. PTML model of enzyme subclasses for mining the proteome of biofuel producing microorganisms. J. Proteome Res.,  2019, 18(7), 2735-2746.
[http://dx.doi.org/10.1021/acs.jproteome.8b00949] [PMID: 31081631] 
[84] 
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res.,  2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235] 
[85] 
Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res.,  2019, 47(D1), D339-D343.
[http://dx.doi.org/10.1093/nar/gky1006] [PMID: 30357391] 
[86] 
Quevedo-Tumailli, V.F.; Ortega-Tenezaca, B.; González-Díaz, H. Chromosome gene orientation inversion networks (GOINS) of plasmodium proteome. J. Proteome Res.,  2018, 17(3), 1258-1268.
[http://dx.doi.org/10.1021/acs.jproteome.7b00861] [PMID: 29336158] 
[87] 
Liu, Y.; Tang, S.; Fernandez-Lozano, C.; Munteanu, C.R.; Pazos, A.; Yu, Y.; Tan, Z.; González-Díaz, H. Experimental study and random forest prediction model of microbiome cell surface hydrophobicity. Expert Syst. Appl.,  2017, 72, 306-316.
[http://dx.doi.org/10.1016/j.eswa.2016.10.058] 
[88] 
Barreiro, E.; Munteanu, C.R.; Cruz-Monteagudo, M.; Pazos, A.; González-Díaz, H. Net-net auto machine learning (AUTOML) prediction of complex ecosystems. Sci. Rep.,  2018, 8(1), 12340.
[http://dx.doi.org/10.1038/s41598-018-30637-w] [PMID: 30120369] 
[89] 
Blay, V.; Yokoi, T.; González-Díaz, H. Perturbation theory-machine learning study of zeolite materials desilication. J. Chem. Inf. Model.,  2018, 58(12), 2414-2419.
[http://dx.doi.org/10.1021/acs.jcim.8b00383] [PMID: 30139249] 
[90] 
González-Durruthy, M.; Alberici, L.C.; Curti, C.; Naal, Z.; Atique-Sawazaki, D.T.; Vázquez-Naya, J.M.; González-Díaz, H.; Munteanu, C.R. 
                        Experimental-computational study of carbon nanotube effects on mitochondrial respiration: 
                        
                            in silico
                        
                         nano-qspr machine learning models based on new raman spectra transform with markov-shannon entropy invariants.
                     J. Chem. Inf. Model.,  2017, 57(5), 1029-1044.
[http://dx.doi.org/10.1021/acs.jcim.6b00458] [PMID: 28414908] 
[91] 
González-Durruthy, M.; Castro, M.; Nunes, S.M.; Ventura-Lima, J.; Alberici, L.C.; Naal, Z.; Atique-Sawazaki, D.T.; Curti, C.; Ruas, C.P.; Gelesky, M.A.; Roy, K.; González-Díaz, H.; Monserrat, J.M. QSPR/QSAR-based perturbation theory approach and mechanistic electrochemical assays on carbon nanotubes with optimal properties against mitochondrial fenton reaction experimentally induced by fe2+-overload. Carbon,  2017, 115, 312-330.
[http://dx.doi.org/10.1016/j.carbon.2017.01.002] 
[92] 
González-Durruthy, M.; Werhli, A.V.; Seus, V.; Machado, K.S.; Pazos, A.; Munteanu, C.R.; González-Díaz, H.; Monserrat, J.M. Decrypting strong and weak single-walled carbon nanotubes interactions with mitochondrial voltage-dependent anion channels using molecular docking and perturbation theory. Sci. Rep.,  2017, 7(1), 13271.
[http://dx.doi.org/10.1038/s41598-017-13691-8] [PMID: 29038520] 
[93] 
Diez-Alarcia, R.; Yáñez-Pérez, V.; Muneta-Arrate, I.; Arrasate, S.; Lete, E.; Meana, J.J.; González-Díaz, H. Big data challenges targeting proteins in gpcr signaling pathways; combining ptml-chembl models and [35s]gtpγs binding assays. ACS Chem. Neurosci.,  2019, 10(11), 4476-4491.
[http://dx.doi.org/10.1021/acschemneuro.9b00302] [PMID: 31618004] 
[94] 
Tran, L.T.; Blay, V.; Luang, S.; Eurtivong, C.; Choknud, S.; González-Díaz, H.; Ketudat Cairns, J.R. Engineering faster transglycosidases and their acceptor specificity. Green Chem.,  2019, 21, 2823-2836.
[http://dx.doi.org/10.1039/C9GC00621D] 
Rights & Permissions Print Cite
Article Metrics
24
1
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1568026621666210121153413	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294
Current Topics in Medicinal Chemistry

Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Related Articles

Abstract
